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Joint Models for Longitudinal and Survival Data [ESP72]

Course highlights

EC points

Start date

End date

23 August 2019

Course days

Monday to Friday (5 mornings)

Course time

From 8:45 till 11:45

Faculty

Prof. Dimitris Rizopoulos, PhD

Course fee

€ 470

Location

Erasmus MC, Rotterdam NL

Level

Intermediate

Prerequisites

This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. In addition, basic knowledge of R would be beneficial but is not required.

Disciplines

Biostatistics

Methodology

Course Materials

Online, download instructions will be sent in August by e-mail.

Participants are required to bring their own laptop with the battery fully charged. Before the course instructions will be sent for installing the required software.

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Testimonial

Itai Magodoro

Zimbabwe

Detailed information about this course:

Description

Faculty: Prof. Dimitris Rizopoulos, PhD

Longitudinal and time-to-event outcomes are the main types of outcomes encountered in medical studies. Primary examples of the former are biomarkers or other patient parameters that are measured during follow-up, whereas for the latter examples include the time to relapse of the disease, time to re-operation or time to death. This course introduces a new type of statistical models that can be used to investigate the association structure between longitudinal and survival outcomes.

In terms of software, we will use R and illustrate how these models can be fitted using package JM and JMbayes.

Participants will be expected to bring their own laptop computers to the session, and to have recent versions of R

(http://www.r-project.org/) and of R packages JM

(http://cran.r-project.org/package=JM) and JMbayes

(http://cran.r-project.org/package=JMbayes) already installed on these computers. All necessary computer code will be provided beforehand.

Objectives

Explain when these models should be used in practice and how they can be utilized to extract relevant information from the data.

Introduce the concept of dynamic predictions that has direct applications in personalized medicine.

Participant profile

Professional statisticians, epidemiologists and public health experts, working in applied environments where hierarchical modelling and survival analysis are key issues.